Improving artificial bee colony algorithm using modified nearest neighbor sequence
Kai Li, Hui Wang, Wenjun Wang, Feng Wang, Zhihua Cui
Abstract
Nearest neighbor (NN) is a simple machine learning algorithm, which is often used in classification problems. In this paper, a concept of modified nearest neighbor (MNN) is proposed to strengthen the optimization capability of artificial bee colony (ABC) algorithm. The new approach is called ABC based on modified nearest neighbor sequence (NNSABC). Firstly, MNN is used to construct solution sequences. Unlike the original roulette selection, NNSABC randomly chooses a solution from the corresponding nearest neighbor sequence to generate offspring. Then, two novel search strategies based on the modified nearest neighbor sequence are employed to build a strategy pool. In the optimization process, different search strategies are dynamically chosen from the strategy pool according to the current search status. In order to study the optimization capability of NNSABC, two benchmark sets including 22 classical problems and 28 CEC 2013 complex problems are tested. Experimental results show NNSABC obtains competitive performance when compared with twenty-three other ABCs and evolutionary algorithms.